Neural Networks for Modelling and Control

Neural Networks for Modelling and Control

Neural Networks for Modelling and Control

A revival of interest in Artificial Neural Networks has taken place during the past decade. Research has focused on the ability of these algorithms to learn from their past experiences, generalising the stored information so that it affects the network's response for similar inputs. Adaptive and learning systems are attractive to the control engineer as they can offer significant advantages for modelling and controlling time-varying, complex plants operating in non-stationary environments. This chapter provides an introduction to the ANNs which are commonly used in modelling and control applications and compares their advantages and disadvantages. The text is broadly split into three sections which separately describe the network's modelling abilities, the learning rules and the model evaluation strategies.

Abstract

A revival of interest in Artificial Neural Networks has taken place during the past decade. Research has focused on the ability of these algorithms to learn from their past experiences, generalising the stored information so that it affects the network's response for similar inputs. Adaptive and learning systems are attractive to the control engineer as they can offer significant advantages for modelling and controlling time-varying, complex plants operating in non-stationary environments. This chapter provides an introduction to the ANNs which are commonly used in modelling and control applications and compares their advantages and disadvantages. The text is broadly split into three sections which separately describe the network's modelling abilities, the learning rules and the model evaluation strategies.